While the widespread use of ubiquitously connected devices in Internet of Everything (IoE)
offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one …

In vertical federated learning (VFL), multiple participants can collaborate in training a model
with distributed data features and labels managed by one of them. The cooperation provides …

Federated learning (FL) has attracted widespread attention in the Internet of Things domain
recently. With FL, multiple distributed devices can cooperatively train a global model by …

Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to
build machine learning models together in a distributed fashion. In general, these parties …

Federated learning (FL) algorithm has been widely studied in recent years due to its ability
for sharing data while protecting privacy. However, FL has risks, such as model inversion …

Federated Learning (FL) is a distributed machine learning technique where multiple devices
(such as smartphones or IoT devices) train a shared global model by using their local data …

Vertical federated learning (VFL) is a privacy-preserving distribution learning paradigm that
enables participants, owning different features of the same sample space to train a machine …

Federated Learning (FL) has attracted extraordinary attention from the industry and
academia due to its advantages in privacy protection and collaboratively training on isolated …

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique
which can train a global model in a collaborative way while preserving the private data in the …

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (eg,
organizations or enterprises) to collaboratively build machine learning models with privacy …